Download Mask_rcnn_coco.h5 ((install)) -

git clone https://github.com/matterport/Mask_RCNN.git cd Mask_RCNN Use code with caution. Step 2: Install Dependencies

While the repository is older, the v2.0 weights are the standard pre-trained weights still used for most Mask R-CNN implementations, including those ported to TensorFlow 2.x. 3. How to Use mask_rcnn_coco.h5

Clone the Matterport Mask_RCNN repository to get the necessary architecture files: download mask_rcnn_coco.h5

Instant object detection, instance segmentation, or transfer learning.

To evaluate on the COCO dataset, install pycocotools : pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI . git clone https://github

The original code works best with TensorFlow 1.15. If you are using TensorFlow 2.x, consider using a fork like ahmedfgad/Mask-RCNN-TF2 which also utilizes the same weights.

Place the mask_rcnn_coco.h5 file inside your working directory. Then, load the weights in your script: How to Use mask_rcnn_coco

import os import mrcnn.model as modellib from mrcnn.config import Config # Create configuration class InferenceConfig(Config): NAME = "coco" GPU_COUNT = 1 IMAGES_PER_GPU = 1 NUM_CLASSES = 1 + 80 # 80 classes + 1 background config = InferenceConfig() # Load model model = modellib.MaskRCNN(mode="inference", model_dir=".", config=config) # Load weights COCO_MODEL_PATH = "mask_rcnn_coco.h5" model.load_weights(COCO_MODEL_PATH, by_name=True) print("Weights loaded successfully!") Use code with caution. 4. Troubleshooting and Tips